Classification of Manufacturing Defects in Multicrystalline Solar Cells With Novel Feature Descriptor

Su, Binyi; Chen, Haiyong; Zhu, Yifan; Liu, Weipeng; Liu, Kun

doi:10.1109/tim.2019.2900961

Cited by 72 publications

(15 citation statements)

References 31 publications

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“…At testing, the distance of the grains from the samples to the clusters is used to decide if the grain is defective or not. Similarly, in Su et al [ 22 ], they use a modified Center-Symmetric Local Binary Patterns (CS-LBP) feature descriptor to extract features from the defective areas in the cells, which are then used to train the K-means algorithm. The cluster centroids from training samples are employed to generate global feature vectors to train a classification algorithm, such as a Support Vector Machine (SVM).…”

Section: Related Workmentioning

confidence: 99%

Anomaly Detection and Automatic Labeling for Solar Cell Quality Inspection Based on Generative Adversarial Network

Balzategui

Eciolaza

Maestro-Watson

2021

Sensors

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Quality inspection applications in industry are required to move towards a zero-defect manufacturing scenario, with non-destructive inspection and traceability of 100% of produced parts. Developing robust fault detection and classification models from the start-up of the lines is challenging due to the difficulty in getting enough representative samples of the faulty patterns and the need to manually label them. This work presents a methodology to develop a robust inspection system, targeting these peculiarities, in the context of solar cell manufacturing. The methodology is divided into two phases: In the first phase, an anomaly detection model based on a Generative Adversarial Network (GAN) is employed. This model enables the detection and localization of anomalous patterns within the solar cells from the beginning, using only non-defective samples for training and without any manual labeling involved. In a second stage, as defective samples arise, the detected anomalies will be used as automatically generated annotations for the supervised training of a Fully Convolutional Network that is capable of detecting multiple types of faults. The experimental results using 1873 Electroluminescence (EL) images of monocrystalline cells show that (a) the anomaly detection scheme can be used to start detecting features with very little available data, (b) the anomaly detection may serve as automatic labeling in order to train a supervised model, and (c) segmentation and classification results of supervised models trained with automatic labels are comparable to the ones obtained from the models trained with manual labels.

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Section: Related Workmentioning

confidence: 99%

Anomaly Detection and Automatic Labeling for Solar Cell Quality Inspection Based on Generative Adversarial Network

Balzategui

Eciolaza

Maestro-Watson

2021

Sensors

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“…Other algorithms are introduced to alleviate the PID through detecting PV faults such as partial shading, dust, arcing and hotspots [11,12], yet these algorithms cannot overcome the PID problem, while they can only indicate whether the PV system has an early fault; in other words, ideally, they are classified as PV fault detection algorithms rather than PID mitigation.…”

Section: Introductionmentioning

confidence: 99%

Recovery of Photovoltaic Potential-Induced Degradation Utilizing Automatic Indirect Voltage Source

Dhimish

Badran

2022

IEEE Trans. Instrum. Meas.

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“…These defect could result by many accidents throughout the OSC fabrication process, for instance, by scratches, uneven morphologies, and so on. There are many possible variations of intelligent systems which are trained to detect patterns, extract image features or identify defects on a surface 4‐11 However, in general, bulk defects, interface defects, and interconnect defects can generate shunt and series resistance within the cell, contrarily it is yet uncertain the peculiar effect of many kind of defects on the OSC functionality 12‐14 . For that reason it is crucial to implement methodologies for detection, localization, and identification of physical defects, not only during the production cycle, but also at design time.…”

Section: Introductionmentioning

confidence: 99%

Organic solar cells defects classification by using a new feature extraction algorithm and an EBNN with an innovative pruning algorithm

et al. 2021

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Physical defects reduce the organic solar cells (OSC) functioning. Throughout the OSC fabrication process, the defects can occur, for instance, by scratches or uneven morphologies. In general, bulk defects, interface defects, and interconnect defects can promote shunt and series resistance of the cell. It is crucial to properly detect and classify such defects and their amount in the structure. Correlating such defects with the performance of the cell is important both during the R&D stages to optimize processes, and for mass production stages where defects detection is an integral part of the production line. For the recognition of texture variations in the scanning electron microscope images caused by these defects is crucial the definition of a set of features for texture representation. Because the low‐order Zernike moments can represent the whole shape of the image and the high‐order Zernike moments can describe the detail. Then, in our case, the feature of the image can be represented by a small number of Zernike moments. In fact, the feature set extracted and described by the Zernike moments are not sensitive to the noises and are hardly redundant. So it possible concentrate the signal energy over a set of few vectors. Finally, for classification, an elliptical basis function neural networks was used. The results show effectiveness of the proposed methodology. In fact, we obtained correct classification of 89.3% over testing data set.

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Classification of Manufacturing Defects in Multicrystalline Solar Cells With Novel Feature Descriptor

Cited by 72 publications

References 31 publications

Anomaly Detection and Automatic Labeling for Solar Cell Quality Inspection Based on Generative Adversarial Network

Anomaly Detection and Automatic Labeling for Solar Cell Quality Inspection Based on Generative Adversarial Network

Recovery of Photovoltaic Potential-Induced Degradation Utilizing Automatic Indirect Voltage Source

Organic solar cells defects classification by using a new feature extraction algorithm and an EBNN with an innovative pruning algorithm

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